What are the different robust regression methods and how do they compare?

What are the different robust regression methods and how do they compare?

Robust regression methods are designed to be less sensitive to outliers than traditional regression. Common methods include Huber regression, Least Trimmed Squares, and RANSAC. Would you like a detailed comparison of these methods?

Yes, please provide a comparison of Huber regression and RANSAC.

Huber regression combines the ideas of least squares and absolute loss, making it effective for data with outliers. RANSAC, on the other hand, iteratively selects random subsets of data to fit a model, which can be very robust in the presence of outliers. Huber is generally more efficient with smaller datasets, while RANSAC excels with larger datasets.

Are there any specific scenarios where one method is preferred over the other?

Yes, Huber regression is preferred when you have a moderate number of outliers, while RANSAC is better for datasets with a high proportion of outliers. It really depends on the nature of your data.

Can you suggest any resources for further reading on these methods?

Sure! I recommend checking out 'Robust Statistics: The Approach Based on Influence Functions' by Peter J. Huber and 'An Introduction to Robust Regression' articles available online for a deeper understanding.